Monte carlo localization with mixture proposal distribution

Sebastian Thrun and Dieter Fox

Recently, Monte Carlo localization (MCL) has been applied successfully to state estimation problems in mobile robotics. This paper points out a limitation of MCL that is counter-intuitive, namely that better sensors can yield worse results. An analysis of this problem leads to the formulation of the "dual" MCL algorithm, which works well in cases were MCL fails. Combining both, MCL and its dual, leads to an extremely robust filter that consistently outperforms MCL and dual MCL--as documented by systematic experimental results.

The full paper is available in gzipped Postscript and PDF

@INPROCEEDINGS{Thrun00d,
  AUTHOR         = {Thrun, S. and Fox, D.},
  TITLE          = {Monte Carlo Localization With Mixture Proposal 
                    Distribution},
  YEAR           = {2000},
  BOOKTITLE      = {Proceedings of the AAAI National Conference on 
                    Artificial Intelligence},
  PUBLISHER      = {AAAI},
  ADDRESS        = {Austin, TX}
}